text: #!/usr/bin/env python
  
  """Top level ``eval`` module.
  """
  
  import warnings
  import tokenize
  from pandas.core import common as com
  from pandas.computation.expr import Expr, _parsers, tokenize_string
  from pandas.computation.scope import _ensure_scope
  from pandas.compat import string_types
  from pandas.computation.engines import _engines
  from distutils.version import LooseVersion
  
  
  def _check_engine(engine):
      """Make sure a valid engine is passed.
  
      Parameters
      ----------
      engine : str
  
      Raises
      ------
      KeyError
        * If an invalid engine is passed
      ImportError
        * If numexpr was requested but doesn't exist
      """
      if engine not in _engines:
          raise KeyError('Invalid engine {0!r} passed, valid engines are'
                         ' {1}'.format(engine, list(_engines.keys())))
  
      # TODO: validate this in a more general way (thinking of future engines
      # that won't necessarily be import-able)
      # Could potentially be done on engine instantiation
      if engine == 'numexpr':
          try:
              import numexpr
          except ImportError:
              raise ImportError("'numexpr' not found. Cannot use "
                                "engine='numexpr' for query/eval "
                                "if 'numexpr' is not installed")
          else:
              ne_version = numexpr.__version__
              if ne_version < LooseVersion('2.1'):
                  raise ImportError("'numexpr' version is %s, "
                                    "must be >= 2.1" % ne_version)
  
  
  def _check_parser(parser):
      """Make sure a valid parser is passed.
  
      Parameters
      ----------
      parser : str
  
      Raises
      ------
      KeyError
        * If an invalid parser is passed
      """
      if parser not in _parsers:
          raise KeyError('Invalid parser {0!r} passed, valid parsers are'
                         ' {1}'.format(parser, _parsers.keys()))
  
  
  def _check_resolvers(resolvers):
      if resolvers is not None:
          for resolver in resolvers:
              if not hasattr(resolver, '__getitem__'):
                  name = type(resolver).__name__
                  raise TypeError('Resolver of type %r does not implement '
                                  'the __getitem__ method' % name)
  
  
  def _check_expression(expr):
      """Make sure an expression is not an empty string
  
      Parameters
      ----------
      expr : object
          An object that can be converted to a string
  
      Raises
      ------
      ValueError
        * If expr is an empty string
      """
      if not expr:
          raise ValueError("expr cannot be an empty string")
  
  
  def _convert_expression(expr):
      """Convert an object to an expression.
  
      Thus function converts an object to an expression (a unicode string) and
      checks to make sure it isn't empty after conversion. This is used to
      convert operators to their string representation for recursive calls to
      :func:`~pandas.eval`.
  
      Parameters
      ----------
      expr : object
          The object to be converted to a string.
  
      Returns
      -------
      s : unicode
          The string representation of an object.
  
      Raises
      ------
      ValueError
        * If the expression is empty.
      """
      s = com.pprint_thing(expr)
      _check_expression(s)
      return s
  
  
  def _check_for_locals(expr, stack_level, parser):
      at_top_of_stack = stack_level == 0
      not_pandas_parser = parser != 'pandas'
  
      if not_pandas_parser:
          msg = "The '@' prefix is only supported by the pandas parser"
      elif at_top_of_stack:
          msg = ("The '@' prefix is not allowed in "
                 "top-level eval calls, \nplease refer to "
                 "your variables by name without the '@' "
                 "prefix")
  
      if at_top_of_stack or not_pandas_parser:
          for toknum, tokval in tokenize_string(expr):
              if toknum == tokenize.OP and tokval == '@':
                  raise SyntaxError(msg)
  
  
  def eval(expr, parser='pandas', engine='numexpr', truediv=True,
           local_dict=None, global_dict=None, resolvers=(), level=0,
           target=None, inplace=None):
      """Evaluate a Python expression as a string using various backends.
  
      The following arithmetic operations are supported: ``+``, ``-``, ``*``,
      ``/``, ``**``, ``%``, ``//`` (python engine only) along with the following
      boolean operations: ``|`` (or), ``&`` (and), and ``~`` (not).
      Additionally, the ``'pandas'`` parser allows the use of :keyword:`and`,
      :keyword:`or`, and :keyword:`not` with the same semantics as the
      corresponding bitwise operators.  :class:`~pandas.Series` and
      :class:`~pandas.DataFrame` objects are supported and behave as they would
      with plain ol' Python evaluation.
  
      Parameters
      ----------
      expr : str or unicode
          The expression to evaluate. This string cannot contain any Python
          `statements
          <http://docs.python.org/2/reference/simple_stmts.html#simple-statements>`__,
          only Python `expressions
          <http://docs.python.org/2/reference/simple_stmts.html#expression-statements>`__.
      parser : string, default 'pandas', {'pandas', 'python'}
          The parser to use to construct the syntax tree from the expression. The
          default of ``'pandas'`` parses code slightly different than standard
          Python. Alternatively, you can parse an expression using the
          ``'python'`` parser to retain strict Python semantics.  See the
          :ref:`enhancing performance <enhancingperf.eval>` documentation for
          more details.
      engine : string, default 'numexpr', {'python', 'numexpr'}
  
          The engine used to evaluate the expression. Supported engines are
  
          - ``'numexpr'``: This default engine evaluates pandas objects using
                           numexpr for large speed ups in complex expressions
                           with large frames.
          - ``'python'``: Performs operations as if you had ``eval``'d in top
                          level python. This engine is generally not that useful.
  
          More backends may be available in the future.
  
      truediv : bool, optional
          Whether to use true division, like in Python >= 3
      local_dict : dict or None, optional
          A dictionary of local variables, taken from locals() by default.
      global_dict : dict or None, optional
          A dictionary of global variables, taken from globals() by default.
      resolvers : list of dict-like or None, optional
          A list of objects implementing the ``__getitem__`` special method that
          you can use to inject an additional collection of namespaces to use for
          variable lookup. For example, this is used in the
          :meth:`~pandas.DataFrame.query` method to inject the
          :attr:`~pandas.DataFrame.index` and :attr:`~pandas.DataFrame.columns`
          variables that refer to their respective :class:`~pandas.DataFrame`
          instance attributes.
      level : int, optional
          The number of prior stack frames to traverse and add to the current
          scope. Most users will **not** need to change this parameter.
      target : a target object for assignment, optional, default is None
          essentially this is a passed in resolver
      inplace : bool, default True
          If expression mutates, whether to modify object inplace or return
          copy with mutation.
  
          WARNING: inplace=None currently falls back to to True, but
          in a future version, will default to False.  Use inplace=True
          explicitly rather than relying on the default.
  
      Returns
      -------
      ndarray, numeric scalar, DataFrame, Series
  
      Notes
      -----
      The ``dtype`` of any objects involved in an arithmetic ``%`` operation are
      recursively cast to ``float64``.
  
      See the :ref:`enhancing performance <enhancingperf.eval>` documentation for
      more details.
  
      See Also
      --------
      pandas.DataFrame.query
      pandas.DataFrame.eval
      """
      first_expr = True
      if isinstance(expr, string_types):
          exprs = [e for e in expr.splitlines() if e != '']
      else:
          exprs = [expr]
      multi_line = len(exprs) > 1
  
      if multi_line and target is None:
          raise ValueError("multi-line expressions are only valid in the "
                           "context of data, use DataFrame.eval")
  
      first_expr = True
      for expr in exprs:
          expr = _convert_expression(expr)
          _check_engine(engine)
          _check_parser(parser)
          _check_resolvers(resolvers)
          _check_for_locals(expr, level, parser)
  
          # get our (possibly passed-in) scope
          level += 1
          env = _ensure_scope(level, global_dict=global_dict,
                              local_dict=local_dict, resolvers=resolvers,
                              target=target)
  
          parsed_expr = Expr(expr, engine=engine, parser=parser, env=env,
                             truediv=truediv)
  
          # construct the engine and evaluate the parsed expression
          eng = _engines[engine]
          eng_inst = eng(parsed_expr)
          ret = eng_inst.evaluate()
  
          if parsed_expr.assigner is None and multi_line:
              raise ValueError("Multi-line expressions are only valid"
                               " if all expressions contain an assignment")
  
          # assign if needed
          if env.target is not None and parsed_expr.assigner is not None:
              if inplace is None:
                  warnings.warn(
                      "eval expressions containing an assignment currently"
                      "default to operating inplace.\nThis will change in "
                      "a future version of pandas, use inplace=True to "
                      "avoid this warning.",
                      FutureWarning, stacklevel=3)
                  inplace = True
  
              # if returning a copy, copy only on the first assignment
              if not inplace and first_expr:
                  target = env.target.copy()
              else:
                  target = env.target
  
              target[parsed_expr.assigner] = ret
  
              if not resolvers:
                  resolvers = ({parsed_expr.assigner: ret},)
              else:
                  # existing resolver needs updated to handle
                  # case of mutating existing column in copy
                  for resolver in resolvers:
                      if parsed_expr.assigner in resolver:
                          resolver[parsed_expr.assigner] = ret
                          break
                  else:
                      resolvers += ({parsed_expr.assigner: ret},)
  
              ret = None
              first_expr = False
  
      if not inplace and inplace is not None:
          return target
  
      return ret
  